Increasing the Clinical Utility of the Paced Auditory Serial Addition Test: Normative Data for Standard, Dyad, and Cognitive Fatigability Scoring
Bibliographic record
Abstract
BACKGROUND: No normative data currently exist that would allow clinicians to decide whether the degree of cognitive fatigability (CF) experienced in individuals with neurologic disease is greater than expected when compared with a healthy population. OBJECTIVE: To establish discrete and regression-based normative data for CF as defined by an objective decrement in performance over the course of a cognitive task; namely, the Paced Auditory Serial Addition Test (PASAT). In addition, to develop discrete and regression-based normative data for PASAT performance scores-dyad and percent dyad-for which data do not currently exist. METHOD: One hundred and seventy-eight healthy individuals completed the PASAT as part of a larger neuropsychological battery. PASAT performance scores including total correct responses, total dyads, and percent dyad were calculated. CF scores were calculated by comparing the individuals' performance on the first half (or third) of the test to their performance on the last half (or third) in order to capture any within-task performance decrements over time. RESULTS: Both age- and education-based discrete normative data and demographically adjusted (sex, age, and education) regression-based formulas were established for the PASAT performance scores and the CF scores. CONCLUSION: The development of these normative data will allow for greater interpretation of an individual's performance on the PASAT, beyond just the total correct score, through the use of dyad and percent dyad scores. With respect to CF, these data will allow clinicians to objectively quantify decrements in cognitive performance over time better in individuals with neurologic diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".